The Role of Baseline Total Kidney Volume Growth Rate in Predicting Tolvaptan Efficacy for ADPKD Patients: A Feasibility Study

被引:0
|
作者
Dev, Hreedi [1 ]
Hu, Zhongxiu [1 ]
Blumenfeld, Jon D. [2 ,3 ]
Sharbatdaran, Arman [1 ]
Kim, Yelynn [1 ]
Zhu, Chenglin [1 ]
Shimonov, Daniil [2 ,3 ]
Chevalier, James M. [2 ,3 ]
Donahue, Stephanie [2 ]
Wu, Alan [4 ]
Roychoudhury, Arindam [4 ]
He, Xinzi [1 ]
Prince, Martin R. [1 ,5 ]
机构
[1] Weill Cornell Med, Dept Radiol, New York, NY 10021 USA
[2] Rogosin Inst, New York, NY 10021 USA
[3] Weill Cornell Med, Dept Med, New York, NY 10021 USA
[4] Weill Cornell Med, Dept Populat Hlth, Div Biostat, New York, NY 10021 USA
[5] Columbia Vagelos Coll Phys & Surg, Dept Radiol, New York, NY 10032 USA
关键词
ADPKD; tolvaptan; MRI; urine osmolality; total kidney volume; vasopressin; Mayo imaging classification; artificial intelligence; deep learning; treatment response; POLYCYSTIC KIDNEY; AUTOMATED SEGMENTATION; DISEASE; PERFORMANCE; STAGE;
D O I
10.3390/jcm14051449
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background/Objectives: Although tolvaptan efficacy in ADPKD has been demonstrated in randomized clinical trials, there is no definitive method for assessing its efficacy in the individual patient in the clinical setting. In this exploratory feasibility study, we report a method to quantify the change in total kidney volume (TKV) growth rate to retrospectively evaluate tolvaptan efficacy for individual patients. Treatment-related changes in estimated glomerular filtration rate (eGFR) are also assessed. Methods: MRI scans covering at least 1 year prior to and during treatment with tolvaptan were performed, with deep learning facilitated kidney segmentation and fitting multiple imaging timepoints to exponential growth in 32 ADPKD patients. Clustering analysis differentiated tolvaptan treatment "responders" and "non-responders" based upon the magnitude of change in TKV growth rate. Differences in rate of eGFR decline, urine osmolality, and other parameters were compared between responders and non-responders. Results: Eighteen (56%) tolvaptan responders (mean age 42 +/- 8 years) were identified by k-means clustering, with an absolute reduction in annual TKV growth rate of >2% (mean = -5.1% +/- 2.5% per year). Thirteen (44%) non-responders were identified, with <1% absolute reduction in annual TKV growth rate (mean = +2.4% +/- 2.7% per year) during tolvaptan treatment. Compared to non-responders, tolvaptan responders had significantly higher mean TKV growth rates prior to tolvaptan treatment (7.1% +/- 3.6% per year vs. 3.7% +/- 2.4% per year; p = 0.003) and higher median pretreatment spot urine osmolality (Uosm, 393 mOsm/kg vs. 194 mOsm/kg, p = 0.03), confirmed by multivariate analysis. Mean annual rate of eGFR decline was less in responders than in non-responders (-0.25 +/- 0.04, CI: [-0.27, -0.23] mL/min/1.73 m2 per year vs. -0.40 +/- 0.06, CI: [-0.43, -0.37] mL/min/1.73 m2 per year, p = 0.036). Conclusions: In this feasibility study designed to assess predictors of tolvaptan treatment efficacy in individual patients with ADPKD, we found that high pretreatment levels of annual TKV growth rate and higher pretreatment spot urine osmolality were associated with a responder phenotype.
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页数:13
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